1. Field of the Invention
The present invention relates to preparation of a knowledge base indispensable to an expert system, and more particularly to a learning system for providing to the expert system a mechanism for automatically preparing the knowledge base which has been made by a person heretofore.
2. Description of the Related Art
Heretofore, a machine learning method has been studied by being broadly divided into two methods including a method of learning a rule from data inductively and a method of analyzing an inference process to learn a rule for increasing efficiency.
As an example of the former, J. R. Quinlan, "Induction of Decision Trees", Machine Learning, pp. 81106, 1986 describes a method of inductively learning a rule from data.
As an example of the latter, Tom M. Mitchell, Richard M. Keller and Smadar T. Kedar-Cabelli, "Explanation-Based Generalization: A Unifying View" Machine Learning Vol. 1, No. 1, pp. 47-80, 1986 describes a method of efficiently solving a similar example after learning by storing an application series of knowledge necessary for solution of a specific problem.
Further, as a method utilizing the algorithm similar to the present invention, "Concept Learning from Inference Pattern" by Kenichi Yoshida and Hiroshi Motoda, Journal of Artificial Intelligence Society of Japan, pp. 119-129, July 1992 has been proposed for the purpose of attaining the object similar to the latter.
The prior art has a problem that separate programs for performing the two methods are required when both functions are necessary in a computer.
For example, when there is considered an operation supporting system for a computer which analyzes operation instructions given halfway by the user and infers the subsequent operation to perform the operation automatically, the pure inductive learning method is excessively influenced by "noise due to the operation for processing [a suddenly] incoming electronic mail" [excessively] when the operation history of the computer is analyzed and as a result, it cannot be put into practice. Further, the pure deductive learning requires a knowledge base capable of analyzing all operations or user's intention and cannot be practically realized either.